Noisy Genes

By Kristin Cobb

Genetically identical cells or organisms grown in identical environments will differ phenotypically, because—even with a common script—gene expression is inherently variable, or noisy.

Such noise is counter-intuitive to many molecular biologists, who would expect gene regulation—the process that shapes all life—to run as precisely as a Swiss watch, says Jeff Hasty, PhD, professor of bioengineering at the University of California, San Diego.

Hasty and his colleagues are trying to expose the biological origins of this variability. In the December 22, 2005 issue of Nature, they report their latest finding in yeast cells. Using a combined experimental and computational approach, they found that variability in gene expression is largely due to cells being in slightly different phases of growth and division.

Variability in gene expression can be intrinsic or extrinsic. Intrinsic noise arises within a single gene, because the biochemical reactions involved in transcription and translation—such as chromatin unwinding, nucleoside binding, and mRNA degradation—are stochastic (random) in nature. Extrinsic noise affects multiple genes within one or more cells, for example fluctuations in environmental conditions or in a cell’s global transcription or translation machinery.

“If you could classify the noise into these two different types, it gives you a handle on what might be causing the noise,” Hasty explains.

His team engineered yeast cells with one to five copies of the gene for an easily quantified green fluorescence protein (GFP), and its promoter. As expected, the cells with five copies lit up five times as brightly on average as the cells with one copy. More interestingly, fluctuations in the signals of the different strains were almost completely correlated, whether there were five gene copies or one, suggesting that extrinsic sources of variability dominate—which agrees with findings from other groups, in different experimental systems.

Hasty’s team then tried to pinpoint the biological sources of this extrinsic variability with computer simulation. Early models that included fancy terms for common transcription or environmental factors, “didn’t fit quite right,” Hasty says. Then they tried something more obvious. They started with the one source of (extrinsic) variability that has to be there: the oscillation in gene expression that arises naturally during the cell cycle.

They built a completely deterministic (non-random) mathematical model of population dynamics coupled with gene expression. In their model, virtual yeast cells, in slightly different phases, grow at a fixed rate to a particular size, and then bud off smaller daughter cells; cells in different stages of the cell cycle produce differing amounts of GFP. This model predicted variation in fluorescence production similar to their experimental data. It also predicted a “noise floor”—a lower limit for variation, which was verified in tests of other highly expressed yeast genes.

“It turned out to be a much simpler answer than we were thinking,” Hasty says.

A few added “bells and whistles” improved the model fit—for example, adding random variation in the time to division and the size of the daughter cells—but the core of the model was still mostly deterministic variation.

“Here’s another piece that expands our understanding of the factors that contribute to variability in gene expression,” says Jim Collins, PhD, professor of biomedical engineering at Boston University. “This sets us up nicely as a community to begin to understand how the cell actually deals with this variability.”